Ask HN: What are the capabilities of consumer grade hardware to work with LLMs?

The article discusses the challenges of understanding the capabilities of one's existing hardware for playing with Large Language Models (LLMs) locally, especially due to the lack of clear documentation. It specifically focuses on the potential of a decent gaming PC with a CUDA-compatible GPU (particularly Nvidia) for handling LLMs.

The author seeks to understand the parameter size models that can be loaded for various VRAM sizes (8 GB, 12 GB, 16 GB, and 24 GB) for inference, fine-tuning, and training. They also inquire about the impact of system RAM, considering sizes of 16 GB, 32 GB, 64 GB, and beyond.

Key takeaways:

Playing with LLMs locally can be challenging due to the lack of clear documentation on what existing hardware can do.
There is a need to understand what can be done with a decent gaming PC with a CUDA-compatible GPU, specifically Nvidia, when it comes to LLMs.
The article seeks to understand what parameter size models can be loaded for various VRAM sizes for inference, fine tuning and training respectively.
The VRAM sizes in question are 8 GB, 12 GB, 16 GB and 24 GB, and the system RAM sizes are 16 GB, 32 GB, 64 GB and beyond.

Ask HN: What are the capabilities of consumer grade hardware to work with LLMs?

Key takeaways:

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